scholarly journals Reconstructing the flight kinematics of swarming and mating in wild mosquitoes

2012 ◽  
Vol 9 (75) ◽  
pp. 2624-2638 ◽  
Author(s):  
Sachit Butail ◽  
Nicholas Manoukis ◽  
Moussa Diallo ◽  
José M. Ribeiro ◽  
Tovi Lehmann ◽  
...  
Keyword(s):  
Likelihood Function ◽  
Average Length ◽  
Tracking System ◽  
Three Dimensional ◽  
Frame Rate ◽  
Video Streams ◽  
Camera System ◽  
Multi Target Tracking ◽  
Velocity Information ◽  
Low Frame Rate

We describe a novel tracking system for reconstructing three-dimensional tracks of individual mosquitoes in wild swarms and present the results of validating the system by filming swarms and mating events of the malaria mosquito Anopheles gambiae in Mali. The tracking system is designed to address noisy, low frame-rate (25 frames per second) video streams from a stereo camera system. Because flying A. gambiae move at 1–4 m s −1 , they appear as faded streaks in the images or sometimes do not appear at all. We provide an adaptive algorithm to search for missing streaks and a likelihood function that uses streak endpoints to extract velocity information. A modified multi-hypothesis tracker probabilistically addresses occlusions and a particle filter estimates the trajectories. The output of the tracking algorithm is a set of track segments with an average length of 0.6–1 s. The segments are verified and combined under human supervision to create individual tracks up to the duration of the video (90 s). We evaluate tracking performance using an established metric for multi-target tracking and validate the accuracy using independent stereo measurements of a single swarm. Three-dimensional reconstructions of A. gambiae swarming and mating events are presented.

scholarly journals Multi-camera real-time three-dimensional tracking of multiple flying animals

2010 ◽  
Vol 8 (56) ◽  
pp. 395-409 ◽  
Author(s):  
Andrew D. Straw ◽  
Kristin Branson ◽  
Titus R. Neumann ◽  
Michael H. Dickinson
Keyword(s):  
Real Time ◽  
Animal Movement ◽  
Genetic Manipulation ◽  
Tracking System ◽  
Three Dimensional ◽  
Neural Basis ◽  
Camera System ◽  
Visual Contrast ◽  
Multi Target Tracking ◽  
Experimental Possibility

Automated tracking of animal movement allows analyses that would not otherwise be possible by providing great quantities of data. The additional capability of tracking in real time—with minimal latency—opens up the experimental possibility of manipulating sensory feedback, thus allowing detailed explorations of the neural basis for control of behaviour. Here, we describe a system capable of tracking the three-dimensional position and body orientation of animals such as flies and birds. The system operates with less than 40 ms latency and can track multiple animals simultaneously. To achieve these results, a multi-target tracking algorithm was developed based on the extended Kalman filter and the nearest neighbour standard filter data association algorithm. In one implementation, an 11-camera system is capable of tracking three flies simultaneously at 60 frames per second using a gigabit network of nine standard Intel Pentium 4 and Core 2 Duo computers. This manuscript presents the rationale and details of the algorithms employed and shows three implementations of the system. An experiment was performed using the tracking system to measure the effect of visual contrast on the flight speed of Drosophila melanogaster . At low contrasts, speed is more variable and faster on average than at high contrasts. Thus, the system is already a useful tool to study the neurobiology and behaviour of freely flying animals. If combined with other techniques, such as ‘virtual reality’-type computer graphics or genetic manipulation, the tracking system would offer a powerful new way to investigate the biology of flying animals.

Blink-Spot Projection Method for Fast Three-Dimensional Shape Measurement

2015 ◽  
Vol 27 (4) ◽  
pp. 430-443 ◽  
Author(s):  
Jun Chen ◽  
◽  
Qingyi Gu ◽  
Tadayoshi Aoyama ◽  
Takeshi Takaki ◽  
...  
Keyword(s):  
Projection Method ◽  
Fast Moving ◽  
High Speed ◽  
Degrees Of Freedom ◽  
Moving Objects ◽  
Three Dimensional ◽  
Frame Rate ◽  
Light Illumination ◽  
Camera System ◽  
3D Shape

<div class=""abs_img""> <img src=""[disp_template_path]/JRM/abst-image/00270004/13.jpg"" width=""300"" /> Blink-spot projection method</div> We present a blink-spot projection method for observing moving three-dimensional (3D) scenes. The proposed method can reduce the synchronization errors of the sequential structured light illumination, which are caused by multiple light patterns projected with different timings when fast-moving objects are observed. In our method, a series of spot array patterns, whose spot sizes change at different timings corresponding to their identification (ID) number, is projected onto scenes to be measured by a high-speed projector. Based on simultaneous and robust frame-to-frame tracking of the projected spots using their ID numbers, the 3D shape of the measuring scene can be obtained without misalignments, even when there are fast movements in the camera view. We implemented our method with a high-frame-rate projector-camera system that can process 512 × 512 pixel images in real-time at 500 fps to track and recognize 16 × 16 spots in the images. Its effectiveness was demonstrated through several 3D shape measurements when the 3D module was mounted on a fast-moving six-degrees-of-freedom manipulator. </span>

scholarly journals Erratum to: A Robust Tracking System for Low Frame Rate Video

2015 ◽  
Vol 115 (3) ◽  
pp. 305-305 ◽  
Author(s):  
Xiaoqin Zhang ◽  
Weiming Hu ◽  
Nianhua Xie ◽  
Hujun Bao ◽  
Stephen Maybank
Keyword(s):  
Tracking System ◽  
Frame Rate ◽  
Robust Tracking ◽  
Low Frame Rate

scholarly journals 3D vibration measurements by a virtual-stereo-camera system based on a single low frame rate camera

2018 ◽  
Vol 12 ◽  
pp. 122-129 ◽  
Author(s):  
Sandro Barone ◽  
Paolo Neri ◽  
Alessandro Paoli ◽  
Armando Viviano Razionale
Keyword(s):  
Frame Rate ◽  
Camera System ◽  
Vibration Measurements ◽  
Stereo Camera ◽  
Low Frame Rate

scholarly journals Three-Dimensional Stereo Vision Tracking of Multiple Free-Swimming Fish for Low Frame Rate Video

2021 ◽  
Vol 25 (5) ◽  
pp. 639-646
Author(s):  
Maria Gemel B. Palconit ◽  
Ronnie S. Concepcion II ◽  
Jonnel D. Alejandrino ◽  
Michael E. Pareja ◽  
Vincent Jan D. Almero ◽  
...  
Keyword(s):  
Computing Time ◽  
Three Dimensional ◽  
Computation Time ◽  
Symbolic Regression ◽  
Machine Learning Algorithms ◽  
Frame Rate ◽  
Tracking Accuracy ◽  
Inference System ◽  
Fish Tracking ◽  
Low Frame Rate

Three-dimensional multiple fish tracking has gained significant research interest in quantifying fish behavior. However, most tracking techniques use a high frame rate, which is currently not viable for real-time tracking applications. This study discusses multiple fish-tracking techniques using low-frame-rate sampling of stereo video clips. The fish were tagged and tracked based on the absolute error of the predicted indices using past and present fish centroid locations and a deterministic frame index. In the predictor sub-system, linear regression and machine learning algorithms intended for nonlinear systems, such as the adaptive neuro-fuzzy inference system (ANFIS), symbolic regression, and Gaussian process regression (GPR), were investigated. The results showed that, in the context of tagging and tracking accuracy, the symbolic regression attained the best performance, followed by the GPR, that is, 74% to 100% and 81% to 91%, respectively. Considering the computation time, symbolic regression resulted in the highest computing lag of approximately 946 ms per iteration, whereas GPR achieved the lowest computing time of 39 ms.

scholarly journals A Robust Tracking System for Low Frame Rate Video

2015 ◽  
Vol 115 (3) ◽  
pp. 279-304 ◽  
Author(s):  
Xiaoqin Zhang ◽  
Weiming Hu ◽  
Nianhua Xie ◽  
Hujun Bao ◽  
Stephen Maybank
Keyword(s):  
Tracking System ◽  
Frame Rate ◽  
Robust Tracking ◽  
Low Frame Rate

scholarly journals Intelligent electromagnetic metasurface camera: system design and experimental results

Nanophotonics ◽  
2022 ◽  
Vol 0 (0) ◽  
Author(s):  
Zhuo Wang ◽  
Hongrui Zhang ◽  
Hanting Zhao ◽  
Tie Jun Cui ◽  
Lianlin Li
Keyword(s):  
Three Dimensional ◽  
Experimental Results ◽  
Frame Rate ◽  
Machine Learning Techniques ◽  
Concrete Wall ◽  
Camera System ◽  
Nondestructive Examination ◽  
Reinforced Concrete Wall ◽  
Bayesian Inference Framework ◽  
Smart Community

Abstract Electromagnetic (EM) sensing is uniquely positioned among nondestructive examination options, which enables us to see clearly targets, even when they visually invisible, and thus has found many valuable applications in science, engineering and military. However, it is suffering from increasingly critical challenges from energy consumption, cost, efficiency, portability, etc., with the rapidly growing demands for the high-quality sensing with three-dimensional high-frame-rate schemes. To address these difficulties, we propose the concept of intelligent EM metasurface camera by the synergetic exploitation of inexpensive programmable metasurfaces with modern machine learning techniques, and establish a Bayesian inference framework for it. Such EM camera introduces the intelligence over the entire sensing chain of data acquisition and processing, and exhibits good performance in terms of the image quality and efficiency, even when it is deployed in highly noisy environment. Selected experimental results in real-world settings are provided to demonstrate that the developed EM metasurface camera enables us to see clearly human behaviors behind a 60 cm-thickness reinforced concrete wall with the frame rate in order of tens of Hz. We expect that the presented strategy could have considerable impacts on sensing and beyond, and open up a promising route toward smart community and beyond.

scholarly journals Towards Tracking of Deep Brain Stimulation Electrodes Using an Integrated Magnetometer

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2670
Author(s):  
Thomas Quirin ◽  
Corentin Féry ◽  
Dorian Vogel ◽  
Céline Vergne ◽  
Mathieu Sarracanie ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Mean Absolute Error ◽  
Tracking System ◽  
Three Dimensional ◽  
Absolute Error ◽  
Magnetic Tracking ◽  
Magnetic Camera ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Brain

This paper presents a tracking system using magnetometers, possibly integrable in a deep brain stimulation (DBS) electrode. DBS is a treatment for movement disorders where the position of the implant is of prime importance. Positioning challenges during the surgery could be addressed thanks to a magnetic tracking. The system proposed in this paper, complementary to existing procedures, has been designed to bridge preoperative clinical imaging with DBS surgery, allowing the surgeon to increase his/her control on the implantation trajectory. Here the magnetic source required for tracking consists of three coils, and is experimentally mapped. This mapping has been performed with an in-house three-dimensional magnetic camera. The system demonstrates how magnetometers integrated directly at the tip of a DBS electrode, might improve treatment by monitoring the position during and after the surgery. The three-dimensional operation without line of sight has been demonstrated using a reference obtained with magnetic resonance imaging (MRI) of a simplified brain model. We observed experimentally a mean absolute error of 1.35 mm and an Euclidean error of 3.07 mm. Several areas of improvement to target errors below 1 mm are also discussed.

scholarly journals Multi-sensor three-dimensional Monte Carlo localization for long-term aerial robot navigation

2017 ◽  
Vol 14 (5) ◽  
pp. 172988141773275 ◽  
Author(s):  
Francisco J Perez-Grau ◽  
Fernando Caballero ◽  
Antidio Viguria ◽  
Anibal Ollero
Keyword(s):  
Monte Carlo ◽  
Motion Tracking ◽  
Robot Navigation ◽  
Tracking System ◽  
Three Dimensional ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Localization Algorithm ◽  
Monte Carlo Localization ◽  
Motion Tracking System

This article presents an enhanced version of the Monte Carlo localization algorithm, commonly used for robot navigation in indoor environments, which is suitable for aerial robots moving in a three-dimentional environment and makes use of a combination of measurements from an Red,Green,Blue-Depth (RGB-D) sensor, distances to several radio-tags placed in the environment, and an inertial measurement unit. The approach is demonstrated with an unmanned aerial vehicle flying for 10 min indoors and validated with a very precise motion tracking system. The approach has been implemented using the robot operating system framework and works smoothly on a regular i7 computer, leaving plenty of computational capacity for other navigation tasks such as motion planning or control.

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